from joblib import load
import numpy as np

class Classification(object):
    def __init__(self):
        self.knn = load('knn_classifier.joblib')
        self.scaler = load('feature_scaler.joblib')
        self.le = load('label_encoder.joblib')

    def get_predicter_category(self, new_data):
        """获取分类结果"""
        # 转换为数组并保持特征顺序
        feature_order = [
            'eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin',
            'fcff', 'fcfe', 'tangible_asset', 'bps', 'grossprofit_margin', 'npta'
        ]
        new_value = np.array([[new_data[col] for col in feature_order]])  # 修正集合为列表推导
        
        # 标准化新数据
        new_scaled = self.scaler.transform(new_value)
        
        # 预测分类
        predicted_label = self.knn.predict(new_scaled)
        predicted_category = self.le.inverse_transform(predicted_label)
        return predicted_category[0]

if __name__ == '__main__':
    ci = Classification()
    new_data = {
        'eps': '-0.8309',
        'total_revenue_ps': '11.9673',
        'undist_profit_ps': '7.0209',
        'gross_margin': '11586700000',
        'fcff': '-12879100000',
        'fcfe': '-5263460000',
        'tangible_asset': '179873000000',
        'bps': '20.2568',
        'grossprofit_margin': '8.1152',
        'npta': '-0.5821'
    }
    predicted_category = ci.get_predicter_category(new_data)
    print(predicted_category)
